The importance of the proposed, richer form of lead time conditioning also shows in the evaluation (Figure REF ). We compare it with the vanilla form of conditioning of simply concatenating the lead time index at the input [1]} as well as part of the richer form of conditioning that only includes the additive bias component that shifts the activations in the convolutional layers. The proposed richer form of conditioning significantly improves performance with up to 1.5 CSI points over the default baseline and both the additive and the multiplicative components are important to the final performance.
